Empirical Likelihood in Count Data Models: The Case of Endogenous Regressors

Stefan Boes ()

No 404, Working Papers from University of Zurich, Socioeconomic Institute

Abstract: Recent advances in the econometric modelling of count data have often been based on the generalized method of moments (GMM). However, the two-step GMM procedure may perform poorly in small samples, and several empirical likelihood-based estimators have been suggested alternatively. In this paper I discuss empirical likelihood (EL) estimation for count data models with endogenous regressors. I carefully distinguish between parametric and semi-parametric methods and analyze the properties of the EL estimator by means of a Monte Carlo experiment. I apply the proposed method to estimate the effect of women’s schooling on fertility.

Keywords: Nonparametric likelihood; Poisson model; endogeneity; fertility and education (search for similar items in EconPapers)
JEL-codes: C14 C25 J13 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2004-03
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http://www.soi.uzh.ch/research/wp/2004/wp0404.pdf First version, 2004 (application/pdf)

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Persistent link: http://EconPapers.repec.org/RePEc:soz:wpaper:0404

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